
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase_    :			Dict    =	"""▁"""

lowerCamelCase_    :			List[str]    =	{"""vocab_file""": """spiece.model"""}

lowerCamelCase_    :			Dict    =	{
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""}
}

lowerCamelCase_    :			Tuple    =	{
    """google/pegasus-xsum""": 5_12,
}


lowerCamelCase_    :			Union[str, Any]    =	logging.get_logger(__name__)

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Any          =			VOCAB_FILES_NAMES

			__UpperCamelCase   :    Any          =			VOCAB_FILES_NAMES
			__UpperCamelCase   :    Tuple          =			PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase   :    Optional[Any]          =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase   :    Optional[int]          =			["""input_ids""", """attention_mask"""]

			def __init__(							self			:							List[str]    ,						snake_case_			:							Dict    ,						snake_case_			:							List[str]="<pad>"    ,						snake_case_			:							Union[str, Any]="</s>"    ,						snake_case_			:							str="<unk>"    ,						snake_case_			:							int="<mask_2>"    ,						snake_case_			:							Tuple="<mask_1>"    ,						snake_case_			:							Optional[int]=None    ,						snake_case_			:							List[Any]=103    ,						snake_case_			:							Optional[Dict[str, Any]] = None    ,						**snake_case_			:							Union[str, Any]    ,						):
							UpperCamelCase_:				Tuple						=    offset
							if additional_special_tokens is not None:
											if not isinstance(snake_case_    ,						snake_case_   ):
															raise TypeError(
															    f'''additional_special_tokens should be of type {type(snake_case_   )}, but is'''
															    f''' {type(snake_case_   )}'''   )

											UpperCamelCase_:				List[Any]						=    (
											    ([mask_token_sent] + additional_special_tokens)
											    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
											    else additional_special_tokens
											)
											# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
											additional_special_tokens_extended += [
											    f'''<unk_{i}>''' for i in range(len(snake_case_   )    ,						self.offset - 1   )
											]

											if len(set(snake_case_   )   ) != len(snake_case_   ):
															raise ValueError(
															    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
															    f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''   )
											UpperCamelCase_:				List[Any]						=    additional_special_tokens_extended
							else:
											UpperCamelCase_:				Union[str, Any]						=    [mask_token_sent] if mask_token_sent is not None else []
											additional_special_tokens += [f'''<unk_{i}>''' for i in range(2    ,						self.offset   )]

							UpperCamelCase_:				Any						=    {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    eos_token=snake_case_    ,						unk_token=snake_case_    ,						mask_token=snake_case_    ,						pad_token=snake_case_    ,						mask_token_sent=snake_case_    ,						offset=snake_case_    ,						additional_special_tokens=snake_case_    ,						sp_model_kwargs=self.sp_model_kwargs    ,						**snake_case_    ,						)
							UpperCamelCase_:				List[str]						=    mask_token_sent
							UpperCamelCase_:				Union[str, Any]						=    vocab_file
							UpperCamelCase_:				Union[str, Any]						=    spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(snake_case_   )

							# add special tokens to encoder dict
							UpperCamelCase_:				Dict[int, str]						=    {
							    0: self.pad_token,
							    1: self.eos_token,
							}

							if self.mask_token_sent is not None:
											self.encoder.update(
											    {
											        2: self.mask_token_sent,
											        3: self.mask_token,
											    }   )

							if self.offset > 0:
											# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
											# mask_token_sent is already added to list -> so start at 1
											self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1    ,						self.offset - 1   )}   )

							UpperCamelCase_:				Dict[str, int]						=    {v: k for k, v in self.encoder.items()}

			@property
			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							return len(self.sp_model   ) + self.offset

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				str						=    {self.convert_ids_to_tokens(snake_case_   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab

			def __getstate__(							self			:							Optional[Any]   ):
							UpperCamelCase_:				Optional[Any]						=    self.__dict__.copy()
							UpperCamelCase_:				List[str]						=    None
							return state

			def __setstate__(							self			:							Optional[Any]    ,						snake_case_			:							int   ):
							UpperCamelCase_:				List[Any]						=    d

							# for backward compatibility
							if not hasattr(self    ,						"""sp_model_kwargs"""   ):
											UpperCamelCase_:				int						=    {}

							UpperCamelCase_:				Union[str, Any]						=    spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(self.vocab_file   )

			def    lowerCAmelCase__						(							self			:							Tuple    ,						snake_case_			:							str   ):
							return self.sp_model.encode(snake_case_    ,						out_type=snake_case_   )

			def    lowerCAmelCase__						(							self			:							Union[str, Any]    ,						snake_case_			:							str   ):
							if token in self.decoder:
											return self.decoder[token]
							elif token in self.added_tokens_decoder:
											return self.added_tokens_decoder[token]
							UpperCamelCase_:				int						=    self.sp_model.piece_to_id(snake_case_   )
							return sp_id + self.offset

			def    lowerCAmelCase__						(							self			:							Optional[Any]    ,						snake_case_			:							int   ):
							if index in self.encoder:
											return self.encoder[index]
							elif index in self.added_tokens_encoder:
											return self.added_tokens_encoder[index]
							else:
											UpperCamelCase_:				Optional[int]						=    self.sp_model.IdToPiece(index - self.offset   )
							return token

			def    lowerCAmelCase__						(							self			:							Tuple    ,						snake_case_			:							Tuple   ):
							UpperCamelCase_:				Dict						=    []
							UpperCamelCase_:				Optional[int]						=    """"""
							for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
															out_string += self.sp_model.decode(snake_case_   ) + token
															UpperCamelCase_:				int						=    []
											else:
															current_sub_tokens.append(snake_case_   )
							out_string += self.sp_model.decode(snake_case_   )
							return out_string.strip()

			def    lowerCAmelCase__						(							self			:							Tuple    ,						snake_case_			:							List[str]=False   ):
							return 1

			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							Any   ):
							UpperCamelCase_:				str						=    set(self.all_special_ids   )  # call it once instead of inside list comp
							all_special_ids.remove(self.unk_token_id   )  # <unk> is only sometimes special

							return [1 if x in all_special_ids else 0 for x in seq]

			def    lowerCAmelCase__						(							self			:							int    ,						snake_case_			:							List    ,						snake_case_			:							Optional[List] = None    ,						snake_case_			:							bool = False   ):
							if already_has_special_tokens:
											return self._special_token_mask(snake_case_   )
							elif token_ids_a is None:
											return self._special_token_mask(snake_case_   ) + [1]
							else:
											return self._special_token_mask(token_ids_a + token_ids_a   ) + [1]

			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							Union[str, Any]    ,						snake_case_			:							Optional[int]=None   ):
							if token_ids_a is None:
											return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]




			def    lowerCAmelCase__						(							self			:							Union[str, Any]    ,						snake_case_			:							str    ,						snake_case_			:							Optional[str] = None   ):
							if not os.path.isdir(snake_case_   ):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''   )
											return
							UpperCamelCase_:				Dict						=    os.path.join(
							    snake_case_    ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

							if os.path.abspath(self.vocab_file   ) != os.path.abspath(snake_case_   ) and os.path.isfile(self.vocab_file   ):
											copyfile(self.vocab_file    ,						snake_case_   )
							elif not os.path.isfile(self.vocab_file   ):
											with open(snake_case_    ,						"""wb"""   ) as fi:
															UpperCamelCase_:				Tuple						=    self.sp_model.serialized_model_proto()
															fi.write(snake_case_   )

							return (out_vocab_file,)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase_    :			Union[str, Any]    =	{
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			List[Any]    =	[
							    """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TapasForMaskedLM""",
							    """TapasForQuestionAnswering""",
							    """TapasForSequenceClassification""",
							    """TapasModel""",
							    """TapasPreTrainedModel""",
							    """load_tf_weights_in_tapas""",
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			Optional[Any]    =	[
							    """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFTapasForMaskedLM""",
							    """TFTapasForQuestionAnswering""",
							    """TFTapasForSequenceClassification""",
							    """TFTapasModel""",
							    """TFTapasPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
							from .tokenization_tapas import TapasTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tapas import (
														    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TapasForMaskedLM,
														    TapasForQuestionAnswering,
														    TapasForSequenceClassification,
														    TapasModel,
														    TapasPreTrainedModel,
														    load_tf_weights_in_tapas,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_tapas import (
														    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFTapasForMaskedLM,
														    TFTapasForQuestionAnswering,
														    TFTapasForSequenceClassification,
														    TFTapasModel,
														    TFTapasPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_    :			Any    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






